Figure 5.

Performance metrics of the ensemble voting classifier model according to the addition of explanatory variables. (A) Cohen κ, (B) AUROC, (C) F₁-score, and (D) balanced accuracy of the ensemble voting classifier are shown as the number of explanatory variables in the training sets in descending order of feature importance coefficients. Mean (dot) and SD (error bar) were computed from the data sets by 5-fold cross-validation. The model reached its plateau performance in all 4 metrics with approximately 21 variables. AUROC: area under the receiver operating characteristics curve; RFI: relative feature importance.